Unified Primitive Proxies for Structured Shape Completion
This addresses the problem of recovering missing geometry as primitives for 3D understanding from incomplete data, representing an incremental improvement over existing methods.
The paper tackles structured shape completion by predicting primitives with complete geometry, semantics, and inlier membership in a single feed-forward pass, lowering Chamfer distance by up to 50% and improving normal consistency by up to 7% across benchmarks.
Structured shape completion recovers missing geometry as primitives rather than as unstructured points, which enables primitive-based surface reconstruction. Instead of following the prevailing cascade, we rethink how primitives and points should interact, and find it more effective to decode primitives in a dedicated pathway that attends to shared shape features. Following this principle, we present UniCo, which in a single feed-forward pass predicts a set of primitives with complete geometry, semantics, and inlier membership. To drive this unified representation, we introduce primitive proxies, learnable queries that are contextualized to produce assembly-ready outputs. To ensure consistent optimization, our training strategy couples primitives and points with online target updates. Across synthetic and real-world benchmarks with four independent assembly solvers, UniCo consistently outperforms recent baselines, lowering Chamfer distance by up to 50% and improving normal consistency by up to 7%. These results establish an attractive recipe for structured 3D understanding from incomplete data. Project page: https://unico-completion.github.io.